1. Technical Field
This disclosure relates to the assessment of pavement conditions, including the assessment of defects in pavement.
2. Description of Related Art
Current pavement condition assessment procedures can be very time consuming, laborious, and expensive. In addition, these approaches can pose safety threats to personnel involved in the process.
The roads and highways in the United States are used to travel approximately three trillion miles annually. According to an American Society of Civil Engineers's (ASCE) report card for America's infrastructure, however, the overall evaluation grade for this infrastructure is a D—close to failing. Among all the infrastructure categories, roads have one of the lowest grades, going down from a grade of D to D—from 2005 to 2009.
Driving on roads in need of repair has been estimated to cost U.S. motorists $67 billion a year in extra vehicle repairs and operating costs. In addition to the deleterious effects on vehicles, safety can also be significantly affected by pavement conditions. From 2005 to 2009, a total of 198,546 people died on U.S. highways. In one-third of these traffic fatalities, roadway conditions played a significant role.
Over $150 billion may be needed annually for substantial improvement of highway conditions, but only $70.3 billion per year has been allocated for highway condition improvement.
Conventional pavement condition assessment procedures include manual or semi-automated approaches. In these approaches, trained personnel may survey conditions of road surfaces, detect pavement distresses, measure severity, and classify the distresses. The manual assessment process can be very time consuming, laborious, and expensive. In addition, it can pose safety threats to the personnel involved in the process. Although the assessment may be carried out by trained raters, the result may lack consistency due to subjectivity in the detection and classification processes.
In semi-automated systems, the process of data acquisition may be carried out using vehicles capable of data collection equipped with cameras or laser sensors. However, the condition assessment of these pavements surfaces may still be done manually by trained raters, and thus still be prone to inconsistencies.
Limitations in manual pavement assessment methods have brought about automated defect assessment. In many commercial pavement assessment tools, image processing approaches are employed for defect detection. However, environmental conditions such as light/shadow condition, different background textures, and non-crack patterns can compromise the assessment outcome. Performance evaluation of various commercially available systems show that they may all may have problems with non-crack patterns, which may result in false crack detection.
The developed image processing tools may also be limited in their ability to differentiate between different types of defects, especially when it comes to defects that are problematic, such as potholes, ravelings, and fatigue cracks. Although automated approaches and specially image processing methods have been researched, manual survey methods are still the predominant approach.
One of the tools for improving detection of defects with three dimensional geometry is laser scanners. However, both image-based and laser scanner-based methods can be expensive. In some cases, equipment costs can be a barrier in adopting the automated method in practice, such as when there is still a need for trained raters to undertake the quality assurance process.
Various integrated systems have been devised to facilitate data collection using special vans that can record data while moving with traffic speed. Generally, these systems use video cameras or laser sensors as the main sensor for capturing pavement conditions. Examples include Automated Road Analyzer (ARAN) equipped with area scan cameras and strobe, infrared lighting and Digital Highway Data Vehicle (DHDV) equipped with a laser-based imaging system, PAVUE equipped with multiple video cameras or line scan cameras, and HARRIS equipped with line scan cameras and advanced lighting system.
Komatsu, used in Japan, uses photomultiplier tube technology for capturing pavement surface images. There are also systems such as CREHOS, SIRANO, and GIE. In most of these systems, image processing tools are employed for defect detection. WiseCrax and Automated Distress Analyzer (ADA) are examples of such tools for analysis of pavement data, captured by ARAN and DHDV systems, respectively.
Image processing approaches may be deployed mainly for detecting various types of cracks in pavements. For other pavement quality factors, such as rut-depth and pavement surface roughness, laser scanning and ultrasonic sensors may be deployed to sense the longitudinal and transversal profile of the road, for evaluating and quantifying these factors.
Various efforts have been made to develop or improve automated approaches for pavement surface assessment. Image processing methods have been the focus of the majority of the research studies. However, using image processing for pavement surface assessment can be a challenging task due to noises such as discoloration and diversity in pavement textures. Many researchers have tried to address the challenges in defect detection and classification, and various unsupervised and supervised segmentation approaches have been employed.
Unsupervised approaches have used default pixels on pavement images. Tanaka and Uematsu (1998) employed morphological segmentation using the structural information of the defect. Hu and Zhao (2010) proposed a modified local binary pattern (LBP) approach to increase robustness against noise. Gavilan et al. (2011) adopted a seed-based approach for defect detection, and used a Support Vector Machine (SVM) classifier to distinguish between different pavement texture types. Zhibiao and Yanqing (2010) used counterlet transform on gray-scale images, along with directionality and anisotropy, for reducing the effect of noise on defect detection.
Other research studies employed anisotropy measure and anisotropy diffusion to reduce the noise level on pavement images. Medina et al. (2010) enhanced the segmentation process by combining traditional features and Gabor filters. Wavelet transform techniques were also explored for decomposing pavement images into different subbands for defect detection, isolation, and severity evaluation. Ma et al. (2009) adopted fractional differential and a wavelet transform-based approach to improve the detection process in the presence of noise. Ying and Salari (2010) employed beamlet transform-based technique as an approach that is insensitive to image noise for crack detection.
Supervised approaches are also used to detect defects in pavement images through classification. Neural networks have been used in many studies. Kaseko et al. (1994) compared the performance of a two-stage piecewise linear neural network with Bayes and k-nearest neighbor classifiers. Different types of neural network models including, image-based, histogram-based, and proximity-based were employed and compared by Lee and Lee (2004). Moment features for neural network models were used in some of the studies. Bray et al. (2006) used neural networks for defect detection and classification based on density and histogram features. Li et al. (2011) demonstrated the application of spatial distribution features, including direction and density features, by using a neural network.
Cord and Chambon (2011) employed the AdaBoost technique to distinguish between defect-laden versus non-defect-laden pavement images. Different type of pavement cracks are the main subjects of these image processing research studies in which defect pixels are segmented using thresholding algorithms. However, selecting the appropriate threshold value has been challenging. Although advanced algorithms have been adopted, limited success has been achieved due to challenges such as poor contrast resulting from a reduced image amplitude range, and noises from shadow and lane markings. Moreover, the severity of the defects are only determined by direct measurements in the pavement surface plane.
Potholes and ravelings are other types of pavement distresses which are important, especially for maintenance planning, due to their significant effect on the quality of pavement, and concomitant economic and safety threats to motorists. For these types of distresses, the depth information can also be an important factor in determining the severity and extent of the defect.
Image processing has also been used for detecting potholes in pavement images. The distinct shape (i.e., approximately elliptical) of potholes, darker areas of shadows in potholes, and texture differences between insides and outsides of potholes are features that were used by Koch and Brilakis (2011) to automate detection of potholes in images.
However, due to the 3D geometry of defects like potholes and ravelings, most of the research studies adopted depth information in the assessment process. Stereovision is one of the methods that has been used for acquiring the depth information. Salari and Bao (2011) combined stereovision techniques with conventional image processing methods to use depth information for applying a probabilistic relaxation on captured images and to reduce the noise effect. Wang (2004) and Hou et al. (2007) also proposed stereovision techniques to create 3D surface models of the pavement for condition assessment. They have also discussed the challenges for complete 3D reconstruction by using stereovision methods due to the complexity of feature matching.
Laser scanning systems have also been used for 3D surface data acquisition. Laser sensors are mainly employed for road surface roughness and rutting measurements. Bursanescu et al. (2001) proposed a 3D vision system based on laser sensors to obtain road surface profiles. Moreover, Yu and Salari (2011) presented a laser imaging technique for pothole detection and severity estimation. Li et al. (2010), used a 3D transverse scanning technique based on infrared laser sensors, and proposed a system for 3D surface profile generation for the detection of pavement distortions such as rutting and shoving. Although laser scanning systems may provide highly accurate geometrical data of the pavement profile and distresses, the cost of the sensors may still be relatively high, which may limit their application in routine pavement assessment.
In the United States, various local and federal standards and guidelines have been developed for pavement management. Each these standards defines nomenclature for evaluating visible pavement defects. Description of the defects, possible causes, severity level, and measurement method are the main characteristics that almost all of the standards/manuals cover for pavement distress assessment.
AASHTO (2003), ASTM (1999), and FHWA (2003) are some of the primary standards in the U.S. According to the Federal Highway Administration (FHWA) guidelines, cracks including longitudinal, transverse, and block cracking, are evaluated using three levels of severity. Cracks with a mean width of 6 mm or less are categorized as low-severity cracks. A mean width between 6 mm to 19 mm is the width for moderate-severity cracks. A mean width more than 19 mm is the criterion for high-severity cracks.
For patching distress, which is the replacement of the original pavement, the criteria for severity assessment is rutting depth. The rutting depth intervals for patching are less than 6 mm, 6 mm to 12 mm and more than 12 mm, for low, moderate and high severities, respectively. For potholes which are bowel-shaped with a minimum dimension of 150 mm, depth is again the main criterion for severity. Potholes less than 25 mm in depth are categorized as low severity; potholes 25 mm to 50 mm deep are moderate; and potholes more than 50 mm deep are evaluated as high severity potholes (FHWA, 2003). Except for the cracks, the rest of the defects in pavements are usually categorized based on depth information.
Accordingly, there has been a need for efficient and economical assessment methods that can detect and quantify defects to provide reliable information for the maintenance of critical defects. Critical defects are those that need to be prioritized in the maintenance schedule in order to enhance the safety and reduce vehicle maintenance costs for motorists.